Read in the csv file, explore the data and create summary information.
# read in the csv file
ut_data <- read.csv("~/Downloads/urine_test_data.csv")
#examine structure
dim(ut_data)
## [1] 1000 33
names(ut_data)
## [1] "Sample_ID" "Organism_1" "Organism_2" "Organism_3"
## [5] "Organism_4" "Organism_5" "Organism_6" "Organism_7"
## [9] "Organism_8" "Organism_9" "Organism_10" "Antibiotic_1"
## [13] "Antibiotic_2" "Antibiotic_3" "Antibiotic_4" "Antibiotic_5"
## [17] "Antibiotic_6" "Antibiotic_7" "Antibiotic_8" "Antibiotic_9"
## [21] "Antibiotic_10" "Antibiotic_11" "Antibiotic_12" "Antibiotic_13"
## [25] "Antibiotic_14" "Antibiotic_15" "Antibiotic_16" "Antibiotic_17"
## [29] "Gene_1" "Gene_2" "Gene_3" "Gene_4"
## [33] "Gene_5"
str(ut_data)
## 'data.frame': 1000 obs. of 33 variables:
## $ Sample_ID : chr "Sample_0001" "Sample_0002" "Sample_0003" "Sample_0004" ...
## $ Organism_1 : int 675 692 0 811 708 0 0 0 738 234 ...
## $ Organism_2 : int 291 377 173 0 553 0 64 0 0 278 ...
## $ Organism_3 : int 0 0 0 710 0 0 0 0 0 73 ...
## $ Organism_4 : int 204 0 0 0 0 678 687 0 0 0 ...
## $ Organism_5 : int 666 0 0 0 0 0 0 0 0 0 ...
## $ Organism_6 : int 0 971 25 0 0 0 0 0 0 0 ...
## $ Organism_7 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Organism_8 : int 0 0 0 485 0 0 0 0 0 0 ...
## $ Organism_9 : int 799 0 0 0 0 0 0 0 0 0 ...
## $ Organism_10 : int 0 0 0 0 0 0 659 0 0 0 ...
## $ Antibiotic_1 : chr "R" "R" "R" "R" ...
## $ Antibiotic_2 : chr "R" "R" "R" "R" ...
## $ Antibiotic_3 : chr "R" "R" "R" "R" ...
## $ Antibiotic_4 : chr "S" "S" "R" "S" ...
## $ Antibiotic_5 : chr "R" "R" "S" "R" ...
## $ Antibiotic_6 : chr "R" "R" "R" "R" ...
## $ Antibiotic_7 : chr "S" "R" "S" "S" ...
## $ Antibiotic_8 : chr "R" "R" "R" "S" ...
## $ Antibiotic_9 : chr "R" "R" "R" "R" ...
## $ Antibiotic_10: chr "R" "R" "R" "R" ...
## $ Antibiotic_11: chr "R" "R" "R" "R" ...
## $ Antibiotic_12: chr "R" "R" "S" "S" ...
## $ Antibiotic_13: chr "S" "R" "S" "S" ...
## $ Antibiotic_14: chr "S" "R" "R" "S" ...
## $ Antibiotic_15: chr "R" "S" "S" "R" ...
## $ Antibiotic_16: chr "S" "S" "S" "S" ...
## $ Antibiotic_17: chr "S" "S" "S" "R" ...
## $ Gene_1 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Gene_2 : int 0 1 0 0 0 1 1 0 0 0 ...
## $ Gene_3 : int 0 0 0 0 0 1 1 0 0 0 ...
## $ Gene_4 : int 0 0 0 0 0 0 0 0 0 1 ...
## $ Gene_5 : int 0 0 0 0 0 0 0 0 0 0 ...
summary(ut_data)
## Sample_ID Organism_1 Organism_2 Organism_3
## Length:1000 Min. : 0.0 Min. : 0.0 Min. : 0.0
## Class :character 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
## Mode :character Median : 0.0 Median : 0.0 Median : 0.0
## Mean :190.5 Mean :182.7 Mean :120.9
## 3rd Qu.:350.2 3rd Qu.:358.0 3rd Qu.: 0.0
## Max. :999.0 Max. :997.0 Max. :999.0
## Organism_4 Organism_5 Organism_6 Organism_7
## Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.00
## Median : 0.00 Median : 0.00 Median : 0.00 Median : 0.00
## Mean : 98.72 Mean : 91.23 Mean : 69.51 Mean : 58.12
## 3rd Qu.: 0.00 3rd Qu.: 0.00 3rd Qu.: 0.00 3rd Qu.: 0.00
## Max. :994.00 Max. :988.00 Max. :998.00 Max. :994.00
## Organism_8 Organism_9 Organism_10 Antibiotic_1
## Min. : 0.00 Min. : 0.00 Min. : 0.00 Length:1000
## 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.00 Class :character
## Median : 0.00 Median : 0.00 Median : 0.00 Mode :character
## Mean : 43.16 Mean : 37.65 Mean : 33.11
## 3rd Qu.: 0.00 3rd Qu.: 0.00 3rd Qu.: 0.00
## Max. :992.00 Max. :998.00 Max. :997.00
## Antibiotic_2 Antibiotic_3 Antibiotic_4 Antibiotic_5
## Length:1000 Length:1000 Length:1000 Length:1000
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Antibiotic_6 Antibiotic_7 Antibiotic_8 Antibiotic_9
## Length:1000 Length:1000 Length:1000 Length:1000
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Antibiotic_10 Antibiotic_11 Antibiotic_12 Antibiotic_13
## Length:1000 Length:1000 Length:1000 Length:1000
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Antibiotic_14 Antibiotic_15 Antibiotic_16 Antibiotic_17
## Length:1000 Length:1000 Length:1000 Length:1000
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Gene_1 Gene_2 Gene_3 Gene_4 Gene_5
## Min. :0.000 Min. :0.00 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.00 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.00 Median :0.000 Median :0.000 Median :0.000
## Mean :0.317 Mean :0.24 Mean :0.172 Mean :0.097 Mean :0.038
## 3rd Qu.:1.000 3rd Qu.:0.00 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.00 Max. :1.000 Max. :1.000 Max. :1.000
# Create lists of each group of variables
org_col <- c("Organism_1","Organism_2","Organism_3","Organism_4","Organism_5","Organism_6",
"Organism_7","Organism_8","Organism_9","Organism_10")
antibio_col <- c("Antibiotic_1","Antibiotic_2","Antibiotic_3","Antibiotic_4","Antibiotic_5","Antibiotic_6",
"Antibiotic_7","Antibiotic_8","Antibiotic_9","Antibiotic_10","Antibiotic_11","Antibiotic_12",
"Antibiotic_13","Antibiotic_14","Antibiotic_15","Antibiotic_16","Antibiotic_17")
gene_col <- c("Gene_1","Gene_2","Gene_3","Gene_4","Gene_5")
# make sure Sample_ID is a factor varaible
ut_data$Sample_ID <- factor(ut_data$Sample_ID)
# Add new summary data to the wide dataframe
ut_data$numorg_count <- rowSums(ut_data[org_col]>0)
ut_data$uti_present <- (ut_data$numorg_count>0)
ut_data$numAR_count <- (rowSums(ut_data[antibio_col]=="R"))
ut_data$AR_present <- (ut_data$numAR_count>0)
ut_data$numgene_count <- (rowSums(ut_data[gene_col]))
ut_data$gene_present <- (ut_data$numgene_count>0)
# create long dataframes for graphics
ut_data_org_long <- gather(ut_data[c("Sample_ID",org_col)],
Organism, count, Organism_1:Organism_10, factor_key = TRUE)
ut_data_anti_long <- gather(ut_data[c("Sample_ID",antibio_col)],
Antibiotic, status, Antibiotic_1:Antibiotic_17, factor_key = TRUE)
ut_data_gene_long <- gather(ut_data[c("Sample_ID",gene_col)],
Gene, status, Gene_1:Gene_5, factor_key = TRUE)
# Create dataframe for organism PCA, set rownames at Sample_ID
ut_data_org_pca <- ut_data[org_col]
rownames(ut_data_org_pca) <- ut_data[,1]
This is fairly straightforward. Each graphic drives home that this is simulated data. There is a beutiful stepped percentage of antibiotic resistance. Same goes for the Genes. I assume “Genes” are bacterial genes and not germline human genes in the subject noting that individual’s PGx metabolism. The Gene’s by subject grph is really crowded, so I seperated it into 10 of ~100 subjects each. The Organism cell count distribution is again ver similar, with just a slight variation in subjects/count for each organism.
# Percent Antibiotic Resistance
ggplot(ut_data_anti_long, aes(x=Antibiotic, y = prop.table(stat(count)), fill=factor(status)),
label = scales::percent(prop.table(stat(count)))) +
geom_bar(colour = "black", position = "fill") + scale_y_continuous(labels = scales::percent) +
scale_fill_brewer(palette = "Pastel1") + scale_x_discrete(guide = guide_axis(n.dodge = 2)) +
geom_text(aes(label=signif(..count.. / tapply(..count.., ..x.., sum)[as.character(..x..)], digits=3)),
stat="count", position=position_fill(vjust=0.5)) +
labs(y="Proportion", fill = "AR Status") + ggtitle("Percentage of samples with Antibiotic resistance for each Antibiotic") +
theme(plot.title = element_text(color="black",size = 24, face = "bold"),axis.title = element_text(color="black",size = 12, face = "bold"))
#Table format
table(ut_data_anti_long$Antibiotic, ut_data_anti_long$status=="R")
##
## FALSE TRUE
## Antibiotic_1 98 902
## Antibiotic_2 158 842
## Antibiotic_3 168 832
## Antibiotic_4 213 787
## Antibiotic_5 280 720
## Antibiotic_6 321 679
## Antibiotic_7 366 634
## Antibiotic_8 408 592
## Antibiotic_9 444 556
## Antibiotic_10 512 488
## Antibiotic_11 546 454
## Antibiotic_12 565 435
## Antibiotic_13 604 396
## Antibiotic_14 667 333
## Antibiotic_15 707 293
## Antibiotic_16 759 241
## Antibiotic_17 777 223
# Presence of each gene across samples
ggplot(ut_data_gene_long[], aes(x=Gene, y=Sample_ID )) + geom_tile(aes(fill = factor(status))) +
labs(y="Sample ID", fill = "Gene Presence") + ggtitle("Presence or Absence of Genes 1 through 5 for each sample ") +
scale_fill_manual(values = c("darkblue","lightblue")) +
theme(plot.title = element_text(color="black",size = 24, face = "bold"),axis.title = element_text(color="black",size = 12, face = "bold"))
#Table format
table(ut_data_gene_long$Gene, ut_data_gene_long$status=="1")
##
## FALSE TRUE
## Gene_1 683 317
## Gene_2 760 240
## Gene_3 828 172
## Gene_4 903 97
## Gene_5 962 38
# You cannot read the Sample_IDs on the graph with 1000 rows. Instead lets generate 10 graphs with ~100 rows each.
ggplot(subset(ut_data_gene_long, grepl("^Sample_00",Sample_ID)), aes(x=Gene, y=Sample_ID )) + geom_tile(aes(fill = factor(status))) +
labs(y="Sample ID", fill = "Gene Presence") +ggtitle("Presence or Absence of Genes 1 through 5 for each sample 1:99 ") +
scale_fill_manual(values = c("darkblue","lightblue")) +
theme(plot.title = element_text(color="black",size = 22, face = "bold"),axis.title = element_text(color="black",size = 12, face = "bold"))
ggplot(subset(ut_data_gene_long, grepl("^Sample_01",Sample_ID)), aes(x=Gene, y=Sample_ID )) + geom_tile(aes(fill = factor(status))) +
labs(y="Sample ID", fill = "Gene Presence") +ggtitle("Presence or Absence of Genes 1 through 5 for each sample 100:199 ") +
scale_fill_manual(values = c("darkblue","lightblue")) +
theme(plot.title = element_text(color="black",size = 22, face = "bold"),axis.title = element_text(color="black",size = 12, face = "bold"))
ggplot(subset(ut_data_gene_long, grepl("^Sample_02",Sample_ID)), aes(x=Gene, y=Sample_ID )) + geom_tile(aes(fill = factor(status))) +
labs(y="Sample ID", fill = "Gene Presence") +ggtitle("Presence or Absence of Genes 1 through 5 for each sample 200:299") +
scale_fill_manual(values = c("darkblue","lightblue")) +
theme(plot.title = element_text(color="black",size = 22, face = "bold"),axis.title = element_text(color="black",size = 12, face = "bold"))
ggplot(subset(ut_data_gene_long, grepl("^Sample_03",Sample_ID)), aes(x=Gene, y=Sample_ID )) + geom_tile(aes(fill = factor(status))) +
labs(y="Sample ID", fill = "Gene Presence") +ggtitle("Presence or Absence of Genes 1 through 5 for each sample 300:399") +
scale_fill_manual(values = c("darkblue","lightblue")) +
theme(plot.title = element_text(color="black",size = 22, face = "bold"),axis.title = element_text(color="black",size = 12, face = "bold"))
ggplot(subset(ut_data_gene_long, grepl("^Sample_04",Sample_ID)), aes(x=Gene, y=Sample_ID )) + geom_tile(aes(fill = factor(status))) +
labs(y="Sample ID", fill = "Gene Presence") +ggtitle("Presence or Absence of Genes 1 through 5 for each sample 400:499") +
scale_fill_manual(values = c("darkblue","lightblue")) +
theme(plot.title = element_text(color="black",size = 22, face = "bold"),axis.title = element_text(color="black",size = 12, face = "bold"))
ggplot(subset(ut_data_gene_long, grepl("^Sample_05",Sample_ID)), aes(x=Gene, y=Sample_ID )) + geom_tile(aes(fill = factor(status))) +
labs(y="Sample ID", fill = "Gene Presence") +ggtitle("Presence or Absence of Genes 1 through 5 for each sample 500:599") +
scale_fill_manual(values = c("darkblue","lightblue")) +
theme(plot.title = element_text(color="black",size = 22, face = "bold"),axis.title = element_text(color="black",size = 12, face = "bold"))
ggplot(subset(ut_data_gene_long, grepl("^Sample_06",Sample_ID)), aes(x=Gene, y=Sample_ID )) + geom_tile(aes(fill = factor(status))) +
labs(y="Sample ID", fill = "Gene Presence") +ggtitle("Presence or Absence of Genes 1 through 5 for each sample 600:699") +
scale_fill_manual(values = c("darkblue","lightblue")) +
theme(plot.title = element_text(color="black",size = 22, face = "bold"),axis.title = element_text(color="black",size = 12, face = "bold"))
ggplot(subset(ut_data_gene_long, grepl("^Sample_07",Sample_ID)), aes(x=Gene, y=Sample_ID )) + geom_tile(aes(fill = factor(status))) +
labs(y="Sample ID", fill = "Gene Presence") +ggtitle("Presence or Absence of Genes 1 through 5 for each sample 700:799") +
scale_fill_manual(values = c("darkblue","lightblue")) +
theme(plot.title = element_text(color="black",size = 22, face = "bold"),axis.title = element_text(color="black",size = 12, face = "bold"))
ggplot(subset(ut_data_gene_long, grepl("^Sample_08",Sample_ID)), aes(x=Gene, y=Sample_ID )) + geom_tile(aes(fill = factor(status))) +
labs(y="Sample ID", fill = "Gene Presence") +ggtitle("Presence or Absence of Genes 1 through 5 for each sample 800:899") +
scale_fill_manual(values = c("darkblue","lightblue")) +
theme(plot.title = element_text(color="black",size = 22, face = "bold"),axis.title = element_text(color="black",size = 12, face = "bold"))
ggplot(subset(ut_data_gene_long, grepl("^Sample_09",Sample_ID) | grepl("^Sample_1",Sample_ID)), aes(x=Gene, y=Sample_ID )) +
geom_tile(aes(fill = factor(status))) + labs(y="Sample ID", fill = "Gene Presence") +
ggtitle("Presence or Absence of Genes 1 through 5 for each sample 900:1000") + scale_fill_manual(values = c("darkblue","lightblue")) +
theme(plot.title = element_text(color="black",size = 22, face = "bold"),axis.title = element_text(color="black",size = 12, face = "bold"))
# distributions of each organism across all samples
ggplot(ut_data_org_long, aes(x=count, color=Organism)) + geom_density() +
ggtitle("Distribution of cell counts for each organism") + scale_fill_brewer(palette = "Pastel1") +
theme(plot.title = element_text(color="black",size = 24, face = "bold"),axis.title = element_text(color="black",size = 12, face = "bold"))
#Table format
table(ut_data_org_long$Organism, ut_data_org_long$count>0)
##
## FALSE TRUE
## Organism_1 620 380
## Organism_2 646 354
## Organism_3 766 234
## Organism_4 794 206
## Organism_5 829 171
## Organism_6 848 152
## Organism_7 880 120
## Organism_8 903 97
## Organism_9 929 71
## Organism_10 937 63
If this data were real I would look to see if the oraganism cell counts correlate with each other (they don’t see the ggpairs output). I would calculate the total number of samples with at least one active organism, but also look at the distribution for how many organism are in a common UTI. For this case I assume ANY cells cell count>0 is equivalent to infection. This may not be the case, but it is my baseline assumption for this analysis. 894 samples have at least 1 organism, while 106 samples have no organism on the test. The median and IQR for the number of organisms per sample is 2{1:3}.
I would do the same for Antibiotic resistance to the 17 antibiotics and the 5 genes. It turns out every sample is resistant to at least 4 Antibiotics with median{IQR} resitance to 9{8-11}. This seems odd or impossible since 106 samples have no UTI organisms, and therefore cannot possibly have antibiotic resistance. Again, I am not sure what the “Genes” are counting, but if these are bacterial genes, it may again be impossible for someone with no organism to have genes present. Bothe of these issues would lead me to question the validity of the assay.
# organism cross-correlation
ggpairs(ut_data[org_col])
# organism present
table(ut_data$uti_present)
##
## FALSE TRUE
## 106 894
ggplot(ut_data, aes(x=numorg_count, )) + geom_bar()
summary(ut_data$numorg_count)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 1.000 2.000 1.848 3.000 6.000
# resitance to antibiotics
table(ut_data$AR_present)
##
## TRUE
## 1000
ggplot(ut_data, aes(x=numAR_count, )) + geom_bar()
summary(ut_data$numAR_count)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 4.000 8.000 9.000 9.407 11.000 15.000
# gene present
table(ut_data$gene_present)
##
## FALSE TRUE
## 366 634
ggplot(ut_data, aes(x=numgene_count, )) + geom_bar()
summary(ut_data$numgene_count)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 1.000 0.864 1.000 3.000
# Gene vs Organisms present
addmargins(table(ut_data$gene_present, ut_data$uti_present))
##
## FALSE TRUE Sum
## FALSE 41 325 366
## TRUE 65 569 634
## Sum 106 894 1000
The instructions were very unclear as to what was being predicted. Due to this vagueness, I chose to model both directions, at least for some of the variables. I really don’t think the version predicting antibiotic resistance was working correctly, so I abandoned it after 4 antibiotics. No Organism cell count predicted the presence or absence of Antibiotic resistance for the first four antibiotics.
# While both of these sections could have been run in a loop, I specifically chose to write out each command
# separately to see the exact command implemented for each model.
ut_data$AB1 <- 0
ut_data$AB1[ut_data$Antibiotic_1=="R"] <- 1
ut_data$AB2 <- 0
ut_data$AB2[ut_data$Antibiotic_2=="R"] <- 1
ut_data$AB3 <- 0
ut_data$AB3[ut_data$Antibiotic_3=="R"] <- 1
ut_data$AB4 <- 0
ut_data$AB4[ut_data$Antibiotic_4=="R"] <- 1
AB1_stan <- stan_glm(AB1 ~ Organism_1 + Organism_2 + Organism_3 + Organism_4 + Organism_5 +
Organism_6 + Organism_7 + Organism_8 + Organism_9 + Organism_10,
family = binomial(link = "logit"),prior = NULL, data=ut_data)
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AB1_stan_m <- as.matrix(AB1_stan)
AB2_stan <- stan_glm(AB2 ~ Organism_1 + Organism_2 + Organism_3 + Organism_4 + Organism_5 +
Organism_6 + Organism_7 + Organism_8 + Organism_9 + Organism_10,
family = binomial(link = "logit"),prior = NULL, data=ut_data)
##
## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
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AB2_stan_m <- as.matrix(AB2_stan)
AB3_stan <- stan_glm(AB3 ~ Organism_1 + Organism_2 + Organism_3 + Organism_4 + Organism_5 +
Organism_6 + Organism_7 + Organism_8 + Organism_9 + Organism_10,
family = binomial(link = "logit"),prior = NULL, data=ut_data)
##
## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
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AB3_stan_m <- as.matrix(AB3_stan)
AB4_stan <- stan_glm(AB4 ~ Organism_1 + Organism_2 + Organism_3 + Organism_4 + Organism_5 +
Organism_6 + Organism_7 + Organism_8 + Organism_9 + Organism_10,
family = binomial(link = "logit"),prior = NULL, data=ut_data)
##
## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
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AB4_stan_m <- as.matrix(AB4_stan)
# AB1
posterior_interval(AB1_stan, prob=0.95)
## 2.5% 97.5%
## (Intercept) 1.8539379299 2.5440979150
## Organism_1 -0.0005084820 0.0009080416
## Organism_2 -0.0006473404 0.0008360838
## Organism_3 -0.0008101723 0.0007949352
## Organism_4 -0.0005248155 0.0014626546
## Organism_5 -0.0006831493 0.0012342639
## Organism_6 -0.0007176635 0.0016643302
## Organism_7 -0.0012617447 0.0009373192
## Organism_8 -0.0016127100 0.0007673674
## Organism_9 -0.0015643593 0.0008918133
## Organism_10 -0.0011991572 0.0018155503
plot(AB1_stan)
ppc_dens_overlay(y = AB1_stan$y, yrep = posterior_predict(AB1_stan, draws = 50))
mcmc_trace(AB1_stan_m )
# AB2
posterior_interval(AB2_stan, prob=0.95)
## 2.5% 97.5%
## (Intercept) 1.3921275351 1.9911968192
## Organism_1 -0.0006718512 0.0004500575
## Organism_2 -0.0007126956 0.0004088690
## Organism_3 -0.0003087402 0.0011037011
## Organism_4 -0.0003155872 0.0013095802
## Organism_5 -0.0010070356 0.0003858738
## Organism_6 -0.0003056876 0.0017193868
## Organism_7 -0.0012830476 0.0003657222
## Organism_8 -0.0007265931 0.0016455885
## Organism_9 -0.0009246771 0.0014580556
## Organism_10 -0.0014598865 0.0008007833
plot(AB2_stan)
ppc_dens_overlay(y = AB2_stan$y, yrep = posterior_predict(AB2_stan, draws = 50))
mcmc_trace(AB2_stan_m )
# AB3
posterior_interval(AB3_stan, prob=0.95)
## 2.5% 97.5%
## (Intercept) 1.2677149289 1.831979e+00
## Organism_1 -0.0005104311 5.978137e-04
## Organism_2 -0.0010820721 1.374872e-05
## Organism_3 -0.0004691592 8.931984e-04
## Organism_4 -0.0007251242 7.251541e-04
## Organism_5 -0.0002033521 1.405529e-03
## Organism_6 -0.0002273277 1.656712e-03
## Organism_7 -0.0004273550 1.541926e-03
## Organism_8 -0.0009005421 1.369282e-03
## Organism_9 -0.0003867583 2.223015e-03
## Organism_10 -0.0007015486 1.919641e-03
plot(AB3_stan)
ppc_dens_overlay(y = AB3_stan$y, yrep = posterior_predict(AB3_stan, draws = 50))
mcmc_trace(AB3_stan_m )
# AB4
posterior_interval(AB4_stan, prob=0.95)
## 2.5% 97.5%
## (Intercept) 1.2790369177 1.814430e+00
## Organism_1 -0.0007560362 2.026844e-04
## Organism_2 -0.0008900241 9.891353e-05
## Organism_3 -0.0006856375 4.398353e-04
## Organism_4 -0.0006457228 6.820609e-04
## Organism_5 -0.0006531774 6.536820e-04
## Organism_6 -0.0014128630 -2.069166e-05
## Organism_7 -0.0004770176 1.332487e-03
## Organism_8 -0.0016261302 1.455036e-04
## Organism_9 -0.0013731107 4.479878e-04
## Organism_10 -0.0002911874 2.295085e-03
plot(AB4_stan)
ppc_dens_overlay(y = AB4_stan$y, yrep = posterior_predict(AB4_stan, draws = 50))
mcmc_trace(AB4_stan_m )
This may be the original intention of this exercise. At least this direction has some significant output. For example, Organism2 cell size appears to have a positive association with Antibiotic resistance to AB3 and AB16. Several other organism show slight associations with one or more antibiotics. See the posterior interval tables and interval plots for which may Antibiotics may be significant. Any 95% interval that does not include “0” may be significantly associated with that organism’s cell count.
# While both of these sections could have been run in a loop, I specifically chose to write out each command
# separately to see the exact command implemented for each model.
# Build out models
org1_stan <- stan_glm(Organism_1 ~ Antibiotic_1 + Antibiotic_2 + Antibiotic_3 + Antibiotic_4 + Antibiotic_5 + Antibiotic_6 +
Antibiotic_7 + Antibiotic_8 + Antibiotic_9 + Antibiotic_10 + Antibiotic_11 + Antibiotic_12 +
Antibiotic_13 + Antibiotic_14 + Antibiotic_15 + Antibiotic_16 + Antibiotic_17,
family = gaussian(),prior = NULL, data=ut_data)
##
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org1_stan_m <- as.matrix(org1_stan)
org2_stan <- stan_glm(Organism_2 ~ Antibiotic_1 + Antibiotic_2 + Antibiotic_3 + Antibiotic_4 + Antibiotic_5 + Antibiotic_6 +
Antibiotic_7 + Antibiotic_8 + Antibiotic_9 + Antibiotic_10 + Antibiotic_11 + Antibiotic_12 +
Antibiotic_13 + Antibiotic_14 + Antibiotic_15 + Antibiotic_16 + Antibiotic_17,
family = gaussian(),prior = NULL, data=ut_data)
##
## SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
## Chain 1:
## Chain 1: Gradient evaluation took 1.6e-05 seconds
## Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
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##
## SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
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## Chain 2: Gradient evaluation took 1.1e-05 seconds
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##
## SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
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## Chain 3: Gradient evaluation took 1.1e-05 seconds
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##
## SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
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## Chain 4: Gradient evaluation took 1e-05 seconds
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org2_stan_m <- as.matrix(org2_stan)
org3_stan <- stan_glm(Organism_3 ~ Antibiotic_1 + Antibiotic_2 + Antibiotic_3 + Antibiotic_4 + Antibiotic_5 + Antibiotic_6 +
Antibiotic_7 + Antibiotic_8 + Antibiotic_9 + Antibiotic_10 + Antibiotic_11 + Antibiotic_12 +
Antibiotic_13 + Antibiotic_14 + Antibiotic_15 + Antibiotic_16 + Antibiotic_17,
family = gaussian(),prior = NULL, data=ut_data)
##
## SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
## Chain 1:
## Chain 1: Gradient evaluation took 1.9e-05 seconds
## Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.
## Chain 1: Adjust your expectations accordingly!
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##
## SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
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## Chain 2: Gradient evaluation took 1e-05 seconds
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##
## SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
## Chain 3:
## Chain 3: Gradient evaluation took 1.2e-05 seconds
## Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.12 seconds.
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##
## SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
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## Chain 4: Gradient evaluation took 1e-05 seconds
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org3_stan_m <- as.matrix(org3_stan)
org4_stan <- stan_glm(Organism_4 ~ Antibiotic_1 + Antibiotic_2 + Antibiotic_3 + Antibiotic_4 + Antibiotic_5 + Antibiotic_6 +
Antibiotic_7 + Antibiotic_8 + Antibiotic_9 + Antibiotic_10 + Antibiotic_11 + Antibiotic_12 +
Antibiotic_13 + Antibiotic_14 + Antibiotic_15 + Antibiotic_16 + Antibiotic_17,
family = gaussian(),prior = NULL, data=ut_data)
##
## SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
## Chain 1:
## Chain 1: Gradient evaluation took 1.7e-05 seconds
## Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
## Chain 1: Adjust your expectations accordingly!
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## SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
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## Chain 2: Gradient evaluation took 1.1e-05 seconds
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## SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
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## Chain 3: Gradient evaluation took 1.1e-05 seconds
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## SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
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## Chain 4: Gradient evaluation took 1e-05 seconds
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org4_stan_m <- as.matrix(org4_stan)
org5_stan <- stan_glm(Organism_5 ~ Antibiotic_1 + Antibiotic_2 + Antibiotic_3 + Antibiotic_4 + Antibiotic_5 + Antibiotic_6 +
Antibiotic_7 + Antibiotic_8 + Antibiotic_9 + Antibiotic_10 + Antibiotic_11 + Antibiotic_12 +
Antibiotic_13 + Antibiotic_14 + Antibiotic_15 + Antibiotic_16 + Antibiotic_17,
family = gaussian(),prior = NULL, data=ut_data)
##
## SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
## Chain 1:
## Chain 1: Gradient evaluation took 1.6e-05 seconds
## Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
## Chain 1: Adjust your expectations accordingly!
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## SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
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## SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
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## Chain 3: Gradient evaluation took 1e-05 seconds
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## SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
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## Chain 4: Gradient evaluation took 1.1e-05 seconds
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org5_stan_m <- as.matrix(org5_stan)
org6_stan <- stan_glm(Organism_6 ~ Antibiotic_1 + Antibiotic_2 + Antibiotic_3 + Antibiotic_4 + Antibiotic_5 + Antibiotic_6 +
Antibiotic_7 + Antibiotic_8 + Antibiotic_9 + Antibiotic_10 + Antibiotic_11 + Antibiotic_12 +
Antibiotic_13 + Antibiotic_14 + Antibiotic_15 + Antibiotic_16 + Antibiotic_17,
family = gaussian(),prior = NULL, data=ut_data)
##
## SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
## Chain 1:
## Chain 1: Gradient evaluation took 2e-05 seconds
## Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.2 seconds.
## Chain 1: Adjust your expectations accordingly!
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## SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
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## SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
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org6_stan_m <- as.matrix(org6_stan)
org7_stan <- stan_glm(Organism_7 ~ Antibiotic_1 + Antibiotic_2 + Antibiotic_3 + Antibiotic_4 + Antibiotic_5 + Antibiotic_6 +
Antibiotic_7 + Antibiotic_8 + Antibiotic_9 + Antibiotic_10 + Antibiotic_11 + Antibiotic_12 +
Antibiotic_13 + Antibiotic_14 + Antibiotic_15 + Antibiotic_16 + Antibiotic_17,
family = gaussian(),prior = NULL, data=ut_data)
##
## SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
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org7_stan_m <- as.matrix(org7_stan)
org8_stan <- stan_glm(Organism_8 ~ Antibiotic_1 + Antibiotic_2 + Antibiotic_3 + Antibiotic_4 + Antibiotic_5 + Antibiotic_6 +
Antibiotic_7 + Antibiotic_8 + Antibiotic_9 + Antibiotic_10 + Antibiotic_11 + Antibiotic_12 +
Antibiotic_13 + Antibiotic_14 + Antibiotic_15 + Antibiotic_16 + Antibiotic_17,
family = gaussian(),prior = NULL, data=ut_data)
##
## SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
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org8_stan_m <- as.matrix(org8_stan)
org9_stan <- stan_glm(Organism_9 ~ Antibiotic_1 + Antibiotic_2 + Antibiotic_3 + Antibiotic_4 + Antibiotic_5 + Antibiotic_6 +
Antibiotic_7 + Antibiotic_8 + Antibiotic_9 + Antibiotic_10 + Antibiotic_11 + Antibiotic_12 +
Antibiotic_13 + Antibiotic_14 + Antibiotic_15 + Antibiotic_16 + Antibiotic_17,
family = gaussian(),prior = NULL, data=ut_data)
##
## SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
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org9_stan_m <- as.matrix(org9_stan)
org10_stan <- stan_glm(Organism_10 ~ Antibiotic_1 + Antibiotic_2 + Antibiotic_3 + Antibiotic_4 + Antibiotic_5 + Antibiotic_6 +
Antibiotic_7 + Antibiotic_8 + Antibiotic_9 + Antibiotic_10 + Antibiotic_11 + Antibiotic_12 +
Antibiotic_13 + Antibiotic_14 + Antibiotic_15 + Antibiotic_16 + Antibiotic_17,
family = gaussian(),prior = NULL, data=ut_data)
##
## SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
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org10_stan_m <- as.matrix(org10_stan)
# model diagnostics
# org1
posterior_interval(org1_stan, prob=0.95)
## 2.5% 97.5%
## (Intercept) 148.561030 316.82328
## Antibiotic_1S -80.773704 50.59366
## Antibiotic_2S -47.125269 55.88995
## Antibiotic_3S -52.719355 45.82423
## Antibiotic_4S -21.992968 73.40344
## Antibiotic_5S -48.366729 37.84137
## Antibiotic_6S -70.116416 10.07100
## Antibiotic_7S -47.772259 30.85727
## Antibiotic_8S -67.040447 12.12567
## Antibiotic_9S -48.977096 28.86469
## Antibiotic_10S -27.953967 46.26708
## Antibiotic_11S -17.222817 61.62823
## Antibiotic_12S -34.652821 44.42883
## Antibiotic_13S -5.376953 71.28476
## Antibiotic_14S -55.656754 27.37090
## Antibiotic_15S -58.952653 20.60584
## Antibiotic_16S -61.481357 25.96089
## Antibiotic_17S -68.906872 21.60202
## sigma 294.408777 321.84675
plot(org1_stan)
ppc_dens_overlay(y = org1_stan$y, yrep = posterior_predict(org1_stan, draws = 50))
mcmc_trace(org1_stan_m )
# org2
posterior_interval(org2_stan, prob=0.95)
## 2.5% 97.5%
## (Intercept) 45.632984 203.66634
## Antibiotic_1S -66.582505 53.91644
## Antibiotic_2S -36.551453 62.75136
## Antibiotic_3S 9.291554 107.47275
## Antibiotic_4S -16.712582 74.14336
## Antibiotic_5S -30.862366 52.76299
## Antibiotic_6S -47.668667 28.32247
## Antibiotic_7S -13.838549 62.30070
## Antibiotic_8S -9.999097 64.29174
## Antibiotic_9S -25.943635 50.78890
## Antibiotic_10S -16.271467 55.78870
## Antibiotic_11S -24.797408 47.12190
## Antibiotic_12S -53.942924 23.04457
## Antibiotic_13S -32.265771 41.57003
## Antibiotic_14S -70.609540 10.22694
## Antibiotic_15S -67.230700 13.98810
## Antibiotic_16S 9.934913 96.07911
## Antibiotic_17S -40.190365 49.66852
## sigma 283.425535 309.89830
plot(org2_stan)
ppc_dens_overlay(y = org2_stan$y, yrep = posterior_predict(org2_stan, draws = 50))
mcmc_trace(org2_stan_m )
# org3
posterior_interval(org3_stan, prob=0.95)
## 2.5% 97.5%
## (Intercept) -24.792487 122.617380
## Antibiotic_1S -48.374383 61.913713
## Antibiotic_2S -61.924801 24.099692
## Antibiotic_3S -55.855668 28.217835
## Antibiotic_4S -32.534469 47.270015
## Antibiotic_5S -10.504173 62.823014
## Antibiotic_6S -17.417647 52.279349
## Antibiotic_7S -61.440670 5.055894
## Antibiotic_8S -21.963485 43.880799
## Antibiotic_9S -16.075355 49.694738
## Antibiotic_10S -12.579160 52.519018
## Antibiotic_11S -13.106675 53.397456
## Antibiotic_12S -10.174790 55.810490
## Antibiotic_13S -2.826854 63.505808
## Antibiotic_14S -17.136014 52.529003
## Antibiotic_15S -37.356858 35.146998
## Antibiotic_16S -29.141389 44.177109
## Antibiotic_17S -50.378788 28.901201
## sigma 250.732125 273.664602
plot(org3_stan)
ppc_dens_overlay(y = org3_stan$y, yrep = posterior_predict(org3_stan, draws = 50))
mcmc_trace(org3_stan_m )
# org4
posterior_interval(org4_stan, prob=0.95)
## 2.5% 97.5%
## (Intercept) 37.50771 167.925604
## Antibiotic_1S -70.42824 25.867928
## Antibiotic_2S -63.07578 18.449287
## Antibiotic_3S -35.54123 43.026462
## Antibiotic_4S -26.97607 43.240501
## Antibiotic_5S -45.20993 19.379569
## Antibiotic_6S -45.61242 16.676562
## Antibiotic_7S -42.48861 17.224468
## Antibiotic_8S -15.97429 43.852300
## Antibiotic_9S -10.29065 46.070218
## Antibiotic_10S -22.92659 35.675769
## Antibiotic_11S -46.20813 12.238117
## Antibiotic_12S -55.83398 2.823017
## Antibiotic_13S -60.87290 -2.240137
## Antibiotic_14S -24.94709 35.714574
## Antibiotic_15S -17.59120 48.507002
## Antibiotic_16S -28.55925 40.270616
## Antibiotic_17S -7.68056 60.712081
## sigma 222.67655 243.184061
plot(org4_stan)
ppc_dens_overlay(y = org4_stan$y, yrep = posterior_predict(org4_stan, draws = 50))
mcmc_trace(org3_stan_m )
# org5
posterior_interval(org5_stan, prob=0.95)
## 2.5% 97.5%
## (Intercept) 42.881346 172.552675
## Antibiotic_1S -61.933061 35.237422
## Antibiotic_2S -19.345562 58.629444
## Antibiotic_3S -63.971585 11.028006
## Antibiotic_4S -29.422738 41.105211
## Antibiotic_5S -44.919179 22.572244
## Antibiotic_6S -18.801922 44.491955
## Antibiotic_7S -49.777320 9.544217
## Antibiotic_8S -33.527487 25.023617
## Antibiotic_9S -10.716713 47.397048
## Antibiotic_10S -29.802663 29.059225
## Antibiotic_11S -40.876747 16.285832
## Antibiotic_12S -42.648114 15.333082
## Antibiotic_13S -21.867940 36.248435
## Antibiotic_14S -8.733794 54.949986
## Antibiotic_15S -31.222603 30.730773
## Antibiotic_16S -35.691241 33.241095
## Antibiotic_17S -60.560148 11.633385
## sigma 223.879910 244.552707
plot(org5_stan)
ppc_dens_overlay(y = org5_stan$y, yrep = posterior_predict(org5_stan, draws = 50))
mcmc_trace(org5_stan_m )
# org6
posterior_interval(org6_stan, prob=0.95)
## 2.5% 97.5%
## (Intercept) 37.210025 146.666346
## Antibiotic_1S -54.609004 26.636336
## Antibiotic_2S -54.694546 14.640465
## Antibiotic_3S -55.320849 11.167338
## Antibiotic_4S 4.921670 66.901235
## Antibiotic_5S -53.937240 2.562963
## Antibiotic_6S -30.812687 22.037263
## Antibiotic_7S -36.426617 15.617986
## Antibiotic_8S -36.950712 15.431381
## Antibiotic_9S -42.024225 7.641835
## Antibiotic_10S -33.690604 16.507243
## Antibiotic_11S -27.859988 21.258911
## Antibiotic_12S -14.290543 35.104322
## Antibiotic_13S -34.228422 16.672481
## Antibiotic_14S -9.757856 42.799020
## Antibiotic_15S -34.649056 19.815140
## Antibiotic_16S -41.083679 17.008380
## Antibiotic_17S -13.778317 45.580985
## sigma 191.205804 209.238510
plot(org6_stan)
ppc_dens_overlay(y = org6_stan$y, yrep = posterior_predict(org6_stan, draws = 50))
mcmc_trace(org6_stan_m )
# org7
posterior_interval(org7_stan, prob=0.95)
## 2.5% 97.5%
## (Intercept) -11.5702116 89.020982
## Antibiotic_1S -30.8678603 46.596678
## Antibiotic_2S -12.3383602 49.692878
## Antibiotic_3S -42.8090063 17.331219
## Antibiotic_4S -41.7323448 14.608259
## Antibiotic_5S -8.1456094 41.322318
## Antibiotic_6S -18.7879786 29.953864
## Antibiotic_7S -21.9417804 24.430812
## Antibiotic_8S 12.1481628 59.569921
## Antibiotic_9S -26.8669286 19.701259
## Antibiotic_10S -28.3509000 17.864265
## Antibiotic_11S -16.4360893 30.909100
## Antibiotic_12S -43.2904812 2.590936
## Antibiotic_13S -28.8328483 17.223930
## Antibiotic_14S -23.2038846 25.246259
## Antibiotic_15S -20.9705861 28.942315
## Antibiotic_16S 0.2753775 55.201414
## Antibiotic_17S -40.4527816 15.393842
## sigma 177.0263919 193.825527
plot(org7_stan)
ppc_dens_overlay(y = org7_stan$y, yrep = posterior_predict(org7_stan, draws = 50))
mcmc_trace(org7_stan_m )
# org8
posterior_interval(org8_stan, prob=0.95)
## 2.5% 97.5%
## (Intercept) 0.8745323 90.33461
## Antibiotic_1S -17.0828283 52.07886
## Antibiotic_2S -34.0927836 20.42635
## Antibiotic_3S -28.6690885 23.96184
## Antibiotic_4S -2.8293807 45.81140
## Antibiotic_5S -28.2676644 15.18263
## Antibiotic_6S -25.0909020 17.53140
## Antibiotic_7S -29.2209754 10.74233
## Antibiotic_8S -19.1039339 20.30916
## Antibiotic_9S -22.7659497 17.33438
## Antibiotic_10S -29.3884420 10.85260
## Antibiotic_11S -24.3960134 15.26541
## Antibiotic_12S -9.9360647 29.13487
## Antibiotic_13S -22.1419914 20.05771
## Antibiotic_14S -28.9586875 13.20044
## Antibiotic_15S -13.3480327 31.29439
## Antibiotic_16S -19.9832191 27.55874
## Antibiotic_17S -24.7729437 23.38354
## sigma 151.4579820 165.11507
plot(org8_stan)
ppc_dens_overlay(y = org8_stan$y, yrep = posterior_predict(org8_stan, draws = 50))
mcmc_trace(org8_stan_m )
# org9
posterior_interval(org9_stan, prob=0.95)
## 2.5% 97.5%
## (Intercept) 5.598690 92.507592
## Antibiotic_1S -19.810706 45.326416
## Antibiotic_2S -31.233184 22.687343
## Antibiotic_3S -38.551346 12.282911
## Antibiotic_4S -9.857663 36.277329
## Antibiotic_5S -18.817993 26.280605
## Antibiotic_6S -33.120437 9.764766
## Antibiotic_7S -14.810295 25.591481
## Antibiotic_8S -2.646243 37.317936
## Antibiotic_9S -17.393277 20.773321
## Antibiotic_10S -21.716969 17.537265
## Antibiotic_11S -29.016573 10.973076
## Antibiotic_12S -24.236124 14.313160
## Antibiotic_13S -25.368655 13.276704
## Antibiotic_14S -14.837650 27.351963
## Antibiotic_15S -29.127212 13.361596
## Antibiotic_16S -24.795596 20.978165
## Antibiotic_17S -28.563199 16.740810
## sigma 149.337489 163.565888
plot(org9_stan)
ppc_dens_overlay(y = org9_stan$y, yrep = posterior_predict(org9_stan, draws = 50))
mcmc_trace(org5_stan_m )
# org10
posterior_interval(org10_stan, prob=0.95)
## 2.5% 97.5%
## (Intercept) -35.9013617 42.795322
## Antibiotic_1S -29.1014796 29.494250
## Antibiotic_2S -9.6387919 38.696756
## Antibiotic_3S -28.7231806 19.153707
## Antibiotic_4S -36.6594017 8.129336
## Antibiotic_5S -17.5981422 21.458348
## Antibiotic_6S -17.2700651 21.813087
## Antibiotic_7S -27.9759260 8.402205
## Antibiotic_8S -30.3259571 7.102179
## Antibiotic_9S -19.0340040 16.708526
## Antibiotic_10S -15.6325176 20.109410
## Antibiotic_11S -23.2676708 12.313713
## Antibiotic_12S -12.1132161 23.249988
## Antibiotic_13S 3.4137411 40.293597
## Antibiotic_14S -10.0675832 27.365963
## Antibiotic_15S -0.4125649 39.550908
## Antibiotic_16S -24.4483779 18.252756
## Antibiotic_17S -10.8168252 31.789947
## sigma 137.6069001 150.272634
plot(org10_stan)
ppc_dens_overlay(y = org10_stan$y, yrep = posterior_predict(org10_stan, draws = 50))
mcmc_trace(org10_stan_m )
The PCA analysis shows that this is random simulated data with very little underlying structure. The first 10 principal components have equal weight across their eigenvalues with a range of 11.4 to 8.5 across all 10 components.
ut_org_results <- prcomp(ut_data_org_pca, scale = TRUE)
biplot(ut_org_results, scale = 0, xlabs = rep("x",1000))
var_explained <- ut_org_results$sdev^2/sum(ut_org_results$sdev^2)
var_explained
## [1] 0.11384763 0.11195903 0.10779085 0.10443637 0.10205856 0.09728071
## [7] 0.09425264 0.09278389 0.09021369 0.08537663
#create scree plot
qplot(c(1:10), var_explained) +
geom_line() +
xlab("Principal Component") +
ylab("Variance Explained") +
ggtitle("Scree Plot") +
ylim(0, 1)
Overall, this is a very mediocre coding test if the priority for this position is analyzing clinical trials data. It really doesn’t help that most tests are negative, and some of the data makes no physiologic sense. The MCMC Bayesian analysis is not something that most Biostatisticians would ever really use. Especially since you will get similar results (at least identifying the same resistant Antibiotics) using regular linear regression. In my experience, it can be very difficult to get results accepted if you use overly complicated or less common statistical methods.
#glm model
org2_glm <- glm(Organism_2 ~ Antibiotic_1 + Antibiotic_2 + Antibiotic_3 + Antibiotic_4 + Antibiotic_5 + Antibiotic_6 +
Antibiotic_7 + Antibiotic_8 + Antibiotic_9 + Antibiotic_10 + Antibiotic_11 + Antibiotic_12 +
Antibiotic_13 + Antibiotic_14 + Antibiotic_15 + Antibiotic_16 + Antibiotic_17,
family = gaussian(), data=ut_data)
summary(org2_glm)
##
## Call:
## glm(formula = Organism_2 ~ Antibiotic_1 + Antibiotic_2 + Antibiotic_3 +
## Antibiotic_4 + Antibiotic_5 + Antibiotic_6 + Antibiotic_7 +
## Antibiotic_8 + Antibiotic_9 + Antibiotic_10 + Antibiotic_11 +
## Antibiotic_12 + Antibiotic_13 + Antibiotic_14 + Antibiotic_15 +
## Antibiotic_16 + Antibiotic_17, family = gaussian(), data = ut_data)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 123.768 42.194 2.933 0.00343 **
## Antibiotic_1S -6.480 31.618 -0.205 0.83767
## Antibiotic_2S 12.400 25.817 0.480 0.63112
## Antibiotic_3S 57.226 25.146 2.276 0.02308 *
## Antibiotic_4S 28.091 23.063 1.218 0.22351
## Antibiotic_5S 12.185 21.000 0.580 0.56188
## Antibiotic_6S -8.814 20.233 -0.436 0.66322
## Antibiotic_7S 24.717 19.512 1.267 0.20554
## Antibiotic_8S 26.984 19.226 1.404 0.16077
## Antibiotic_9S 12.226 19.011 0.643 0.52031
## Antibiotic_10S 19.707 18.876 1.044 0.29673
## Antibiotic_11S 11.280 18.936 0.596 0.55153
## Antibiotic_12S -15.182 19.038 -0.797 0.42537
## Antibiotic_13S 4.128 19.264 0.214 0.83038
## Antibiotic_14S -29.553 20.153 -1.466 0.14286
## Antibiotic_15S -25.776 20.712 -1.245 0.21360
## Antibiotic_16S 52.883 22.063 2.397 0.01672 *
## Antibiotic_17S 4.666 22.683 0.206 0.83708
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 87769.82)
##
## Null deviance: 88161184 on 999 degrees of freedom
## Residual deviance: 86189961 on 982 degrees of freedom
## AIC: 14240
##
## Number of Fisher Scoring iterations: 2
anova(org2_glm, test='LR' )
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: Organism_2
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 999 88161184
## Antibiotic_1 1 3718 998 88157466 0.83693
## Antibiotic_2 1 35568 997 88121898 0.52439
## Antibiotic_3 1 410294 996 87711604 0.03061 *
## Antibiotic_4 1 172521 995 87539084 0.16092
## Antibiotic_5 1 25459 994 87513625 0.59018
## Antibiotic_6 1 5903 993 87507722 0.79537
## Antibiotic_7 1 151675 992 87356047 0.18865
## Antibiotic_8 1 126050 991 87229997 0.23076
## Antibiotic_9 1 41955 990 87188042 0.48932
## Antibiotic_10 1 116739 989 87071302 0.24879
## Antibiotic_11 1 29416 988 87041886 0.56264
## Antibiotic_12 1 62609 987 86979277 0.39834
## Antibiotic_13 1 10390 986 86968887 0.73080
## Antibiotic_14 1 144910 985 86823977 0.19882
## Antibiotic_15 1 123642 984 86700335 0.23527
## Antibiotic_16 1 506660 983 86193674 0.01628 *
## Antibiotic_17 1 3713 982 86189961 0.83704
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# compared to:
posterior_interval(org2_stan, prob=0.95)
## 2.5% 97.5%
## (Intercept) 45.632984 203.66634
## Antibiotic_1S -66.582505 53.91644
## Antibiotic_2S -36.551453 62.75136
## Antibiotic_3S 9.291554 107.47275
## Antibiotic_4S -16.712582 74.14336
## Antibiotic_5S -30.862366 52.76299
## Antibiotic_6S -47.668667 28.32247
## Antibiotic_7S -13.838549 62.30070
## Antibiotic_8S -9.999097 64.29174
## Antibiotic_9S -25.943635 50.78890
## Antibiotic_10S -16.271467 55.78870
## Antibiotic_11S -24.797408 47.12190
## Antibiotic_12S -53.942924 23.04457
## Antibiotic_13S -32.265771 41.57003
## Antibiotic_14S -70.609540 10.22694
## Antibiotic_15S -67.230700 13.98810
## Antibiotic_16S 9.934913 96.07911
## Antibiotic_17S -40.190365 49.66852
## sigma 283.425535 309.89830